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Passive source localization using importance sampling based on TOA and FOA measurements

Frontiers of Information technology & Electronic engineering 2017年第8期18卷 1167-1179页

作者： rui-rui LIU Yun-long WANG Jie-xin YIN ding WANG Ying WU National Digital Switching System Engineering & Technology Research Center Zhengzhou 450001 China

Passive source localization via a maximum likelihood （ML） estimator can achieve a high accuracy but involves high calculation burdens, especially when based on time-of-arrival and frequency-of-arrival measurements for its internal nonlinearity and nonconvex nature. In this paper, we use the Pincus theorem and Monte Carlo importance sampling （MCIS） to achieve an approximate global solution to the ML problem in a computationally efficient manner. The main contribution is that we construct a probability density function （PdF） of Gaussian distribution, which is called an important function for efficient sampling, to approximate the ML estimation related to complicated distributions. The improved performance of the proposed method is at- tributed to the optimal selection of the important function and also the guaranteed convergence to a global maximum. This process greatly reduces the amount of calculation, but an initial solution estimation is required resulting from Taylor series expansion. However, the MCIS method is robust to this prior knowledge for point sampling and correction of importance weights. Simulation results show that the proposed method can achieve the Cram6r-rao lower bound at a moderate Gaussian noise level and outper- forms the existing methods.

关键词： Passive source localization Time of arrival （TOA） Frequency of arrival （FOA） Monte Carlo importance sampling（MCIS） Maximum likelihood （ML）

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Change detection in Semantic Level for SAr Images

Change Detection in Semantic Level for SAR Images

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International Conference on Image, Vision and Computing (ICIVC)

作者： Tianqi Mao Wei Liu Yongjun Zhao Jie Huang China National Digital System Engineering and Technological R&D Center Zhengzhou China

ISBN: (纸本)9781538649923

Considering that the traditional change detection algorithms only focus on extracting the change area but ignore the change content identification, a novel change detection framework for synthetic aperture radar (SAr) images is proposed. The framework integrates the merits of unsupervised and supervised learning to detect changes in semantic level. First, the residual convolutional auto-encoder (rCAE) is designed to convert SAr image slices to the histogram representation. Then, we calculate the difference vectors and extract the change area by their norms. Finally, we classify the difference vectors of change region and identify the content of change. Experimental results indicate that the proposed method achieves significantly performance improvement over existing algorithms.

关键词： Synthetic aperture radar Semantics Training Feature extraction Convolution Histograms Marine vehicles

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Identification of conductive leakage signal in power cable based on Multi-Classification PSO-SVM 5

Identification of conductive leakage signal in power cable b...

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The 5th International Symposium on Electromagnetic Compatibility

作者： Zhisheng Qian Changlin Zhou Junping Cheng Qinmin Wang China national digital switch system engineering & technological R & D center

ISBN: (纸本)9781509051861;9781509051854

In order to effectively identify and extract the red signal from the leakage signal in the computer's power line,a method based on particle swarm optimization(PSO) is proposed to optimize the multi classification support vector machine(SVM).Firstly,the conductive leakage signal is *** the SVM penalty parameter and kernel parameter are optimized by PSO,the conduction leakage signal is trained and ***,the classification performance of the un-optimized SVM is compared with *** result shows that this method has higher classification rate than the grid search *** classification and recognition rate reaches 87.23%.

关键词： conductive leakage power cable PSO SVM

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A dHr-Based Security-Critical Task Scheduling Algorithm for Heterogeneous Processing system

A DHR-Based Security-Critical Task Scheduling Algorithm for ...

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IEEE International Conference on Computer and Communications

作者： Ye Shengzhao Zhao Bo Wei Shuai Yin Congyue Wang Sunan SoC Lab China National Digital Switching System Engineering & Technological R&D Center Zhengzhou China College of Communication & Engineering Shenzhen Polytechnic Schools Shenzhen China

ISBN: (纸本)9781538683408

Mimic defense techniques can solve unknown vulnerabilities as well as backdoors without prior knowledge based on dHr structure. Compared with traditional defense methods, dHr structure brings dynamic and heterogeneous factors into systems to enhance security. In this paper, we discuss the security-critical scheduling problem in dHr-based processing system and propose MHF algorithm according to the characters of heterogeneous processing nodes with different performance. While ensuring task schedulability, MHF algorithm improves the average heterogeneity of processing nodes as high as possible when allocating redundant versions of a task. Simulation results show that the average heterogeneity of MHF algorithm is 22.7% higher than that of rF algorithm when tasks' guarantee ratio approaches 100%.

关键词： Task analysis Security real-time systems redundancy Software Scheduling algorithms

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rNN deep reinforcement Learning for routing Optimization

RNN Deep Reinforcement Learning for Routing Optimization

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International Conference on Computer and Communications (ICCC)

作者： Penghao Sun Junfei Li Julong Lan Yuxiang Hu Xin Lu China National Digital Switching System Engineering & Technological R&D Center (NDSC) Zhengzhou China China Electronics Technology Group Corporation 32th Research Institute Shanghai China

ISBN: (纸本)9781538683408

routing optimization has been discussed in network design for a long time. In recent years, new methods of routing strategy based on reinforcement Learning are being considered. In this paper, we propose a reinforcement learning based smart agent that can optimize routing strategy without human experience. Our proposed scheme is based on the collection of the traffic intensity in switches and the usage of a recurrent Neural Network based deep reinforcement learning model to train the agent. Simulation result shows that the proposed scheme can adjust the routing strategy dynamically according to the network condition and outperforms the traditional shortest path routing after trained.

关键词： routing reinforcement learning Neural networks Telecommunication traffic Training delays Optimization

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An Intelligent Evaluation Model for decision Scheme Based on deep Learning

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Journal of Physics: Conference Series 2019年第3期1176卷

作者： Wei Ou Zhaohui Yi Lan Cheng Ying Liao The Border and Coastal Defense College Urumqi China China National Digital Switching System Engineering & Technological R&D Center Zhengzhou China

Studying and constructing the intelligent decision-making model for simulation entity can effectively improve the credibility and immersion of wargaming, in which intelligent evaluation of decision scheme is the key module. To lower the reasoning complexity and shorten the decision time, an intelligent evaluation model based on Stacked Auto-Encoder (SAE) is proposed, which simulates the reasoning mode of human decision-making, and learns commander's knowledge and experience through unsupervised and supervised training. Then, to improve the robustness and generalization ability of the deep learning model, a de-noising training method and sparsity constraints are introduced. Finally, simulation experiment is carried out to verify the scientificity and effectiveness of the proposed model.

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GLOBAL CLIMATE

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Bulletin of the American Meteorological Society 2024年第8期105卷 S12-S155页

作者： dunn, r.J.H. Blannin, J. Gobron, N. Miller, J.B. Willett, K.M. Met Office Hadley Centre Exeter United Kingdom European Commission Joint Research Centre Ispra Italy NOAA Global Monitoring Laboratory Boulder Colorado European Centre for Medium-Range Weather Forecasts Reading United Kingdom University of Maryland College Park Maryland European Centre for Medium-Range Weather Forecasts Bonn Germany Department of Meteorology and National Centre for Earth Observation University of Reading Reading United Kingdom Hampton University Hampton Virginia National Research Institute for Agriculture Food and Environment (INRAE) CARRTEL Université Savoie Mont Blanc Thonon les Bains France Graduate School of Agriculture Osaka Metropolitan University Sakai Japan NOAA/NESDIS National Centers for Environmental Information Asheville North Carolina Science Faculty Universidad Nacional de Colombia Bogotá Colombia University of Bremen Bremen Germany Centro de Investigaciones sobre Desertificación – Spanish National Research Council (CSIC-UV-GVA) Valencia Spain Hydro-Climate Extremes Lab (H-CEL) Ghent University Ghent Belgium Laboratoire des Sciences du Climat et de l’Environnement (LSCE) LSCE/IPSL CEA-CNRS-UVSQ Gif-sur-Yvette France Instituto de Geografía Pontificia Universidad Católica de Valparaíso Valparaíso Chile Physical Science and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia University of Reading Reading United Kingdom Institute Pierre Simon Laplace (IPSL) Guyancourt France School of Engineering Newcastle University Newcastle-upon-Tyne United Kingdom Université Paris Cité Institut de physique du globe de Paris CNRS IGN F-75005 Paris France Univ Gustave Eiffel ENSG IGN F-77455 Marne-la-Vallée France Laboratoire EDYTEM CNRS/Université Savoie Mont-Blanc Le Bourget-du-Lac France Global Modeling and Assimilation Office NASA Goddard Space Flight Center Greenbelt Maryland Universität Hamburg Hamburg Germany Climate Dynamics Lab University of St Andrews St Andrews United Kingdom Ea

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Characteristic Analysis and Modeling for the Near Field radiation of FPGA 5

Characteristic Analysis and Modeling for the Near Field Radi...

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The 5th International Symposium on Electromagnetic Compatibility

作者： Junping Cheng Changlin Zhou daojie Yu Zhisheng Qian Mengjuan Chai China national digital switch system engineering & technological R&D center

ISBN: (纸本)9781509051861;9781509051854

To predict the radiation emission of the FPGA,the radiation emission model for the chip is proposed with ICEM and the simulation result is also presented in this *** linear near-field surface scan system(LNFS) following electromagnetic compatibility standard is applied to measure the *** is illustrated that the simulation results agree with the measurements,so the value of the model for practical application is verified.

关键词： electromagnetic compatibility(EMC) radiation emission(rC) LNFS FPGA

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Binary hole transport layer enables stable perovskite solar cells with PCE exceeding 24%

DeCarbon

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deCarbon 2023年 1卷

作者： Xiao Chen Bing Guo Zeyu Zhang Bo Zhang Xinzhi Zu Nabonswende Aida Nadege Ouedraogo Jiyeon Oh Yongjoon Cho George Omololu Odunmbaku Kun Chen Yongli Zhou Shanshan Chen Changduk Yang Juan du Kuan Sun MOE Key Laboratory of Low-grade Energy Utilization Technologies and Systems School of Energy & Power Engineering Chongqing University Chongqing 400044 China School of Physics and Optoelectronic Engineering Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences Hangzhou 310024 China State Key Laboratory of High Field Laser Physics and CAS Center for Excellence in Ultra-intense Laser Science Shanghai Institute of Optics and Fine Mechanics (SIOM) Chinese Academy of Sciences (CAS) Shanghai 201800 China State Key Laboratory of Power Transmission Equipment & System Security and New Technology School of Energy & Power Engineering Chongqing University Chongqing 400044 China School of Energy and Chemical Engineering Perovtronics Research Center Low Dimensional Carbon Materials Center Ulsan National Institute of Science and Technology (UNIST) 50 UNIST-gil Ulju-gun Ulsan 44919 South Korea CTF Solar Zur Wetterwarte50 Haus 303 Dresden 01109 Germany R&D Center JA Solar Holdings Co. Ltd. Yangzhou 225131 China

Hygroscopic dopant in hole transport layer (HTL) is a key factor contributing to moisture-induced perovskite degradation and the resulting performance loss over time. This poses obstacles to the commercialization of perovskite solar cells (PSCs). Herein, we mixed two popular hole transport materials, i.e., [2,2′,7,7′-tetrakis(N,N-di-p-methoxyphenyl-amine)9,9′-spirobifluorene] (Spiro-OMeTAd) and poly (3-hexylthiophene-2,5-diyl) (P3HT), to form a binary mixed HTL. due to the presence of hydrophobic P3HT component, the mixed HTL exhibits improved moisture resistance. In addition, P3HT demonstrates a great ability to interact with the dopants, which changes π-π packing orientation of P3HT from edge-on to face-on and improves its crystallinity, thus increasing hole mobility and hole extraction capability of the mixed HTL. As a result, PSCs equipped with the Spiro-OMeTAd/P3HT mixed HTL exhibit a champion power conversion efficiency (PCE) up to 24.3% and superior operational stability. The cells without encapsulation can maintain 90% initial efficiency after storage in dark ambient conditions (30% rH) for 1200 h. These results suggest that constructing Spiro-OMeTAd/P3HT mixed HTL is a promising strategy to meet the future photovoltaic applications demands with low-cost as well as excellent efficiency and device stability.

关键词： Perovskite solar cells Hole transport materials Morphology Polymer Moisture stability

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INTrOdUCTION

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Bulletin of the American Meteorological Society 2024年第8期105卷 S1-S11页

作者： Boyer, T. Blunden, J. dunn, r.J.H. NOAA/NESDIS National Centers for Environmental Information Silver Spring Maryland NOAA/NESDIS National Centers for Environmental Information Asheville North Carolina Met Office Hadley Centre Exeter United Kingdom European Centre for Medium-Range Weather Forecasts Reading United Kingdom University of Maryland College Park Maryland Scripps Institution of Oceanography University of California San Diego La Jolla California NOAA/NWS National Centers for Environmental Prediction Climate Prediction Center College Park Maryland Brandenburg University of Technology (BTU) Cottbus-Senftenberg Germany Servicio Meteorológico Nacional Buenos Aires Argentina NOAA/OAR Physical Sciences Laboratory Boulder Colorado European Centre for Medium-Range Weather Forecasts Bonn Germany Center for Geophysical Research and School of Physics University of Costa Rica San José Costa Rica Department of Meteorology and National Centre for Earth Observation University of Reading Reading United Kingdom Centro Nacional de Monitoramento e Alertas de Desastres Naturais CEMADEN São Paulo Brazil Université Grenoble Alpes Institut des Géosciences de l’Environnement IRD CNRS Grenoble INP Grenoble France Hampton University Hampton Virginia Seychelles Meteorological Authority Mahe Seychelles Section for Glaciers Ice and Snow Norwegian Water Resources and Energy Directorate Oslo Norway National Research Institute for Agriculture Food and Environment (INRAE) CARRTEL Université Savoie Mont Blanc Thonon les Bains France Graduate School of Agriculture Osaka Metropolitan University Sakai Japan Science Faculty Universidad Nacional de Colombia Bogotá Colombia University of Bremen Bremen Germany NOAA Global Monitoring Laboratory Boulder Colorado Servicio Nacional de Meteorología e Hidrología del Perú Lima Perú Centro de Investigaciones sobre Desertificación – Spanish National Research Council (CSIC-UV-GVA) Valencia Spain Hydro-Climate Extremes Lab (H-CEL) Ghent University Ghent Belgium Department o

In 2023, La Niña conditions that generally prevailed in the eastern Pacific Ocean from mid-2020 into early 2023 gave way to a strong El Niño by October. Atmospheric concentrations of Earth’s major greenhouse gases—carbon dioxide, methane, and nitrous oxide—all increased to record-high levels. The annual global average carbon dioxide concentration in the atmosphere rose to 419.3±0.1 ppm, which is 50% greater than the pre-industrial level. The growth from 2022 to 2023 was 2.8 ppm, the fourth highest in the record since the 1960s. The combined short-term effects of El Niño and the long-term effects of increasing levels of heat-trapping gases in the atmosphere contributed to new records for many essential climate variables reported here. The annual global temperature across land and oceans was the highest in records dating as far back as 1850, with the last seven months (June–december) having each been record warm. Over land, the globally averaged temperature was also record high. dozens of countries reported record or near-record warmth for the year, including China and continental Europe as a whole (warmest on record), India and russia (second warmest), and Canada (third warmest). Intense and widespread heatwaves were reported around the world. In Vietnam, an all-time national maximum temperature record of 44.2°C was observed at Tuong duong on 7 May, surpassing the previous record of 43.4°C at Huong Khe on 20 April 2019. In Brazil, the air temperature reached 44.8°C in Araçuaí in Minas Gerais on 20 November, potentially a new national record and 12.8°C above normal. The effect of rising temperatures was apparent in the cryosphere, where snow cover extent by June 2023 was the smallest in the 56-year record for North America and seventh smallest for the Northern Hemisphere overall. Heatwaves contributed to the greatest average mass balance loss for Alpine glaciers around the world since the start of the record in 1970. due to rapid volume loss beginning in 2021, St. A

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